A Novel Geometrical Method for Discrimination of Normal, Interictal and Ictal EEG Signals
Bibliographic record
Abstract
The electroencephalogram (EEG) signal is known as a nonlinear and complex signal.The EEG signal has very important information about brain activities and disorders which can detect by an accurate Computer-aided diagnosis system.The performance of the Computer-aided diagnosis system directly depends on using features in the classifiers.In this paper, we proposed nonlinear geometrical features for the classification of EEG signals.The normal, interictal and ictal EEG signals of the Bonn university EEG database are plotted in 2D space by a novel approach and considering their patterns, six features namely: area of the octagon (AOO), circle area (CA), the summation of vectors length (SVL), centroid to coordinate center (CTC), circular radius out of triangles (CRT) and triangle area (TA) are extracted on different aspects of distance in Cartesian space.Based on the Kruskal-Wallis statistical test, all of the features were found statistically significant in the discrimination of normal vs. ictal and interictal vs. ictal EEG signals (p-value≈0).Also, the edges of 2D projection EEG signals in the ictal group were sharper than normal and interictal groups.Besides, 2D projection of normal and interictal EEG signals has more regular geometrical shapes than the ictal group.Our proposed features were applied as input on support vector machine (SVM) and k-nearest neighbors (KNN) classifiers which resulted in more than 99% classification accuracy in a tenfold cross-validation strategy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".